In offices, and especially in increasingly common open-space offices, violation of employees' privacy can often become an issue, as tertiary parties may overhear their conversations either intentionally or unintentionally. Because face-to-face, spontaneous conversations among workers can result in a more productive and creative workplace, relieving the concern of being overheard is important.
Existing solutions (e.g., common white noise generators) exploit products that mask conversations with background noise or other audio, which is termed “acoustic conversation shielding”. In general, existing systems for acoustic conversation shielding require that the conversation be conducted in a predetermined location. Sound-masking technologies are routinely used to reduce audio distraction and protect speech privacy in the workspace, such as in an open-plan office, reception area, or a meeting room. For example, conversations in meeting rooms can be protected partly by ceiling-mounted speakers that emit masking sounds. A recent commercial product [Babble®. Sonare Technologies] uses a set of speakers to emit recorded speech to mask a user's phone conversations. However, the targets of known methods are limited to specific situations, such as telephone calls in a cubicle or discussions in a meeting room. These methods do not target spontaneous conversation that could happen at various places in a company, such as a corridor or a casual meeting space. Additionally, existing systems are typically self-contained boxes with a manual volume control. These systems output audio from a single speaker and thus are not capable of adapting to the distribution of people and intrinsic background sound in the environment.
Actuators, such as speakers and lighting, are commonly scattered throughout living environments. As communication and sensing technologies have advanced towards the vision of ubiquitous computing [Weiser, M., “The computer for the 21st century”, Scientific American, 265(3), pp. 94-104, 1991], there are increasing opportunities to take advantage of such distributed actuators by using sensors that make them respond to the environment, thus increasing their utility and/or efficiency. Technologies exploiting networked clusters of sensors have been developed to realize a broad range of applications. In particular, wireless sensor networks are expected to be deployed essentially everywhere (e.g., embedded in everyday objects to realize the dream of ubiquitous computing or unobtrusively collecting data on the environment), as the cost of the deployment will drop due to their denser integration and increasing energy efficiency [Hill. J., Szewczyk. R., Woo. A., Hollar, S., Culler, D., and Pister, K., “System architecture directions for network sensors”, Architectural Support for Programming Languages and Operating Systems, 2000, pp. 93-104; Crossbow Technology]. Today's prototypes of such wireless sensors are tools for building applications that explore the vision of ubiquitous sensor infrastructures [Estrin, D., Govindan, G., Heidemann, J., and Kumar, S., “Next century challenges: Scalable coordination in sensor networks”, Mobile Computing and Networking, pages 263-270, 1999].
Thus far, many sensor network applications have been proposed in wildlife and outdoor monitoring, demonstrating scalability and low-power operation [Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J., “Wireless Sensor Networks for Habitat Monitoring,” WSNA '02, September 2002, Atlanta, Ga., USA, pp. 88-97]. Other researchers have demonstrated workspace applications of sensor networks, such as determining whether conference rooms are occupied using motion sensors [Conner, W. S., Chhabra, J., Yarvis, M., and Krishnamurthy, L., “Experimental evaluation of topology control and synchronization for in-building sensor network applications”, Mobile Networks and Applications, Vol. 10, Issue 4, 2005, pp. 545-562], while others have demonstrated home monitoring systems using wireless sensors [Fogarty, J., Au, C., and Hudson, S. E., “Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition”, Proc. of the 19th Annual ACM Symposium on User interface Software and Technology UIST 2006, Montreux, Switzerland, Oct. 15-18, 2006, pages 91-100]. These applications are generally aimed at monitoring what is happening or has happened in locations where it is costly or impractical for people to observe and collect data in person.
Indoor location awareness technology is one of the major needs of ubiquitous computing. A good overview of location technologies is found in Hightower, J., and Borriello, G., “Location Systems for Ubiquitous Computing”, Computer, 34(8), August 2001, pp. 57-66. Location accuracy has been improved with new technologies such as UWB (ultra wide band); for example, the Ubisense commercial system claims to have up to 15 cm accuracy with active location tag and receivers set at the corners of a room, and UWB systems appropriate for integration into lightweight sensor networks are beginning to appear [K. Mizugaki, et al, “Accurate Wireless Location/Communication System With 22-cm Error Using UWB-IR”, Proc. of the 2007 IEEE Radio and Wireless Symposium, pp. 455-458]. Other recent approaches include adapting GSM [Otsason. V., Varshavsky. A., LaMarca, A., Eyal de Lara, “Accurate GSM Indoor Localization,” Proceedings of the Seventh International Conference on Ubiquitous Computing (UbiComp2005), pp. 141-158, Tokyo, Japan, 2005] and power-line communication [Patel, S. N., Troug, K. N., and Abowd, G. D., “PLP: A Practical Sub-Room-Level Indoor Location System for Domestic Use,” Proceedings of the 8th International Conference on Ubiquitous Computing (UbiComp2006), pp. 441-458, Orange County, USA], which both exploit existing infrastructure. Nonetheless, applications of localization technologies tend to lag and are still generally limited to established ideas such as location-aware guidance [Abowd, G. D., Atkeson, C. G., Hong, J., Long, S., Kooper, R., and Pinkerton, M. “Cyberguide: a mobile context-aware tour guide.” Wireless Networks, 3(5), (October 1997), 421-433]. At a recent mobile computing conference, several location technology experts agreed that researchers in the field should focus more on applications, especially those that combine activity inference with location, instead of inventing a novel location technology [Ebling, M. R., “HotMobile 2006: Mobile Computing Practitioners Interact,” IEEE Pervasive Computing, Volume 5, Issue 4, October-December, pp. 102-105, 2006].